• DocumentCode
    508360
  • Title

    Development of a Rutting Prediction Model through Accelerated Pavement Testing Using Group Method of Data Handling (GMDH)

  • Author

    Chang, Jia-Ruey ; Chao, Sao-Jeng

  • Author_Institution
    Dept. of Civil Eng., MingHsin Univ. of Sci. & Technol., Hsinchu, Taiwan
  • Volume
    3
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    367
  • Lastpage
    371
  • Abstract
    The application of group method of data handling (GMDH) to pavement performance evaluation is relatively new. This paper demonstrates the development of pavement rutting prediction model by using GMDH method. Results from 7 test pavements (264 records) from CRREL´s HVS, a closely controlled full-scale accelerated pavement testing (APT), were employed to establish a rutting prediction model. Additional 2 test pavements (81 records) from CRREL´s HVS were utilized for model verification purposes. GMDH was applied successfully to develop a rutting prediction model that uses wheel load, load repetitions and the pavement structural number (SN) as inputs. The R2 for training data (264 records) and verification data (81 records) are 0.6455 and 0.6288, respectively. The model and algorithms proposed in this study provide a good foundation for further refinement when additional data is available.
  • Keywords
    data handling; group theory; roads; structural engineering computing; accelerated pavement testing; data handling; group method; pavement rutting prediction model; pavement structural number; Chaos; Civil engineering; Data handling; Life estimation; Polynomials; Predictive models; Road transportation; Signal processing algorithms; Testing; Training data; accelerated pavement testing (APT); group method of data handling (GMDH); prediction; rutting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2009. ICNC '09. Fifth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3736-8
  • Type

    conf

  • DOI
    10.1109/ICNC.2009.702
  • Filename
    5366932